Song Wang, Wentong Li, Wenyu Liu, Xiaolu Liu, Jianke Zhu*

This is the official implementation of LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation (CVPR 2023) [Paper] [Video].

Please download the whole nuScene dataset from the official website.

Our project is built with Pytorch >= 1.7 and revised mmdetection3d from BEVFusion.

You can install the tree-filter by:

cd ./map/model/loss/kernels/lib_tree_filter
python3 setup.py build develop

To train the model from scratch, you can run:

cd ./map
bash train.sh # multi-gpu
python train_lidar2map.py # single-gpu

To inference with the obtained checkpoint, you can run:

python test.py --modelf /path/to/ckpt # single-gpu

Thanks for the pioneer work in online map learning: HDMapNet BEVFusion BEVerse

@inproceedings{wang2023lidar2map,
      title={LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation},
      author={Wang, Song and Li, Wentong and Liu, Wenyu and Liu, Xiaolu and Zhu, Jianke},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      pages={5186--5195},
      year={2023}
}